CN111325807A - Encryption and feature extraction method based on JPEG image - Google Patents
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
- G06T9/007—Transform coding, e.g. discrete cosine transform
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20052—Discrete cosine transform [DCT]
Abstract
The invention discloses an encryption and feature extraction method based on a JPEG image, which comprises the steps of encrypting and feature extraction, wherein the encrypting step comprises the steps of analyzing the JPEG image, carrying out exclusive-or encryption on VLI codes of the image, encrypting a quantization table, the feature extraction step comprises the steps of analyzing the JPEG image, converting quantized DCT coefficients into group number indexes of the VLI codes, truncating index values, calculating a state transition probability matrix of each a × b block of each channel, calculating an average state transition probability matrix in each channel and connecting the average state transition probability matrixes to serve as ciphertext image features, and the extracted ciphertext image features can be used for retrieval and classification, are simple and easy to implement, and have high safety, efficiency and accuracy.
Description
Technical Field
The invention belongs to the technical field of pattern recognition, and particularly relates to an encryption and feature extraction method based on a JPEG image.
Background
With the development of content-based image retrieval technology (CBIR), cloud computing and other related technologies, the task of searching images is outsourced to cloud servers, which is a great concern of people. In this way, the image owner can be freed from complex calculations and management, retrieving the desired image over the internet. However, the cloud server may be hacked, and the privacy contained in the image may be leaked, so that the outsourcing of the image brings convenience to people and also causes potential safety hazards.
In order to support similar image search while protecting image content, researchers have proposed a variety of image search encryption methods, which are divided into two categories: one is a privacy protection scheme based on feature encryption, where the image owner first extracts visual features from the image, then protects the image using standard encryption tools, and encrypts the features using specially designed methods to support distance comparisons. Lu et al propose three privacy protection methods based on feature encryption, namely bit plane randomization, random projection and random unary coding. The hamming distance can be directly calculated by using bit plane randomization and random unary coded encrypted feature vectors. The L1 distance can be directly calculated using randomly projected feature vectors. The three methods can well protect features, but compared with a plaintext domain, the retrieval accuracy is influenced.
Another class is privacy protection schemes based on image encryption. In such schemes, the image owner is only responsible for encrypting the image. Other tasks, such as feature extraction, index building, and search operations, may be outsourced to the cloud server, which further reduces the burden on the user. Bellafqira et al propose a privacy protection scheme based on image encryption, which encrypts an image through a homomorphic encryption protocol, and the encrypted image can directly extract SIFT and discrete wavelet transform features. However, since the extracted histogram is also encrypted, the image owner needs to decrypt the histogram and send it back to the cloud server for similarity calculation. Xia et al propose an outsourced CBIR scheme using a word bag model. Encrypting the image by color value replacement, block replacement and intra-block pixel replacement, calculating local histograms from the encrypted image blocks, and clustering the local histograms to generate visible words; the appearance histogram of the visual word is calculated to represent the image, and the retrieval precision is greatly improved. However, encrypting in the spatial domain destroys the correlation between the image pixels. Therefore, the encrypted image is not compressed well.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the above problems, the present invention provides an encryption and feature extraction method based on JPEG images. The method can ensure that the sizes of the original image and the encrypted image are unchanged, and has higher safety; the extracted ciphertext image features can be used for ciphertext image retrieval and have high retrieval precision.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
an encryption and feature extraction method based on JPEG images comprises the following steps:
s1, decoding the original JPEG image to obtain VLI coding and quantization table of the image;
s2, performing stream encryption on VLI codes and quantization tables of the images, and uploading the encrypted images to a cloud server;
s3, after receiving the encrypted image, the cloud server decodes the encrypted JPEG image to obtain VLI code of the image;
s4, restoring the quantized DCT coefficient matrixes Y, U, V by the cloud server, and recording the quantized DCT coefficient matrixes as D respectivelyY,DU,DV；
S5, converting the matrix DY,DU,DVEach element in the code table is converted into a corresponding group number index value according to the VLI code table, and a matrix obtained after conversion is marked as RY,RU,RV；
S6, for the matrix RY,RU,RVThe element in (1) is subjected to truncation processing, and the processed matrix is recorded as R'Y,R′U,R′V；
S7, mixing the matrix R'Y,R′U,R′VDividing the block into a × b, and rearranging a × b elements contained in the block into a sequence S according to a specified direction;
s8, taking the sequence S as a Markov chain, and calculating a state transition probability matrix M of each sequence S;
s9, calculating an average state transition probability matrix M 'within Y, U, V three channels'Y,M′U,M′VAnd connected to form a d-dimensional feature MM.
Further, in step S2, the VLI code is stream-encrypted, and the encryption algorithm is:
where V is the VLI code of the original image, V' is the encrypted VLI code, eVIs the encryption key that is used to encrypt the data,
for xor operation, and ← for assignment operation.
Further, in step S2, performing stream encryption on a quantization table, where the quantization table includes a luminance quantization table and a color difference quantization table, and the encryption algorithm is:
in the formula, aYFor luminance quantization tables, QuanUVAs a chroma quantization table, eYAnd eUVRespectively, the encryption keys of the corresponding quantization tables.
Further, in step S5, D is addedY,DU,DVEach element in the code table is converted into a corresponding group number index value according to the VLI code table, and a matrix obtained after conversion is marked as RY,RU,RVExpressed as:
in the formula, Group index [ x ] is the index value of element x on VLI code table.
Further, in step S6, in order to reduce the dimension of the feature to be extracted and reduce the computational complexity, the matrix needs to be alignedRY,RU,RVThe elements in (1) are subjected to truncation processing, and the matrix after the processing is recorded as R'Y,R′U,R′VThe truncation algorithm is expressed as follows:
wherein T is a truncation threshold, preferably T is 8, rY,rU,rVAre respectively a matrix RY,RU,RVOf (1), r'Y,r′U,r′VAre respectively matrix R'Y,R′U,R′VOf (1).
Further, in step S8, the state transition probability matrix M on the Y channelYThe calculation method comprises the following steps:
wherein x and y are integers and have a value range of [0, T]T is a truncation threshold; stThe t-th rearranged sequence, when q is satisfied, delta (q) is equal to 1, otherwise delta (q) is equal to 0, i is the index of a × b block, i is equal to 1,2, …, blknumY,blknumYIs matrix R'YThe number of inner a × b blocks and for any i,
is a matrix of (T +1) × (T +1) dimensions;
u, V channel state transition probability matrix MU,MVThe calculation method of (2) is the same as that of the Y channel.
Further, in step S9, the average state transition probability matrix calculation method includes:
MM＝[M′Y,M′U,M′V](7)
wherein MM is secretCharacter image feature, blknumY，blknumUAnd blknumVAre respectively matrix R'Y,R′U,R′VThe dimension d of the feature MM is 3 × (T +1) × (T +1), and T is a truncation threshold.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention discloses an encryption and feature extraction method based on JPEG images. The invention relates to a group number index value matrix RY,RU,RVThe elements in the encrypted image are subjected to truncation processing, so that the dimension of the features is reduced, and higher efficiency can be obtained in encrypted image retrieval. The invention calculates the average state transition probability matrix in three channels, and utilizes the matrix R 'after truncation processing'Y,R′U,R′VThe correlation between the medium elements can improve the retrieval precision in the encrypted image retrieval. The ciphertext image features extracted by the method can be used for searching and classifying, are simple and easy to implement, have higher detection precision during searching, and have higher safety, efficiency and accuracy.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a schematic diagram of the composition of a JPEG image of the invention;
FIG. 3 is a VLI code table of the present invention;
FIG. 4 is a diagram of the process of the present invention for converting quantized DCT coefficients into their group number indices;
FIG. 5 is a diagram of the process of the present invention for arranging the group number indices into a new sequence in a given direction;
fig. 6 is a diagram of image encryption effects according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The encryption and feature extraction method based on the JPEG image is a core step for JPEG encrypted image retrieval, and can be used for retrieval and classification by utilizing the feature extraction algorithm provided by the invention. And the higher retrieval precision can be obtained by combining the existing retrieval algorithm (such as a bag-of-words model).
As shown in FIG. 1, the method comprises two parts of encryption and feature extraction, wherein the encryption step comprises ① analyzing a JPEG image, ② carrying out exclusive OR encryption on VLI codes of the image, ③ carrying out encryption on a quantization table, the feature extraction step comprises ① analyzing the JPEG image, ② converting quantized DCT coefficients into group number indexes of the VLI codes, ③ cutting off index values, ④ calculating a state transition probability matrix of each block of each channel, ⑤ calculating an average state transition probability matrix in each channel and connecting the average state transition probability matrixes as ciphertext image features, and the specific flow of each step is described in detail below.
S1, decoding the original JPEG image to obtain VLI coding and quantization table of the image.
In this embodiment, a JPEG image file in an Inria horidays image library is used for description, and as shown in fig. 2, a JPEG image includes a quantization table, a huffman code, and a VLI code.
S2, performing stream encryption on VLI codes and quantization tables of the images, and uploading the encrypted images to a cloud server;
and carrying out stream encryption on the VLI code, wherein the encryption algorithm is as follows:
where V is the VLI code of the original image, V' is the encrypted VLI code, eVIs the encryption key that is used to encrypt the data,
for xor operation, and ← for assignment operation.
Performing stream encryption on a quantization table, wherein the quantization table comprises a brightness quantization table and a color difference quantization table, and the encryption algorithm is as follows:
in the formula, aYFor luminance quantization tables, QuanUVAs a chroma quantization table, eYAnd eUVRespectively, the encryption keys of the corresponding quantization tables.
S3, after receiving the encrypted image, the cloud server decodes the encrypted JPEG image to obtain VLI code of the image; the VLI code table is shown in fig. 3.
S4, restoring the quantized DCT coefficient matrixes Y, U, V by the cloud server, and recording the quantized DCT coefficient matrixes as D respectivelyY,DU,DV。
S5, converting the matrix DY,DU,DVEach element in the code table is converted into a corresponding group number index value according to the VLI code table, and a matrix obtained after conversion is marked as RY,RU,RVExpressed as:
in the formula, Group index [ x ] is the index value of element x on VLI code table. The process of converting the quantized DCT coefficients into their corresponding group number indices is illustrated in fig. 4.
S6, for reducing the dimension of the feature to be extracted and reducing the computational complexity, the matrix R is processedY,RU,RVThe element in (1) is subjected to truncation processing, and the processed matrix is recorded as R'Y,R′U,R′V(ii) a The truncation algorithm is represented as follows:
wherein T is a truncation threshold, and in this embodiment, T is 8, and rY,rU,rVAre respectively a matrix RY,RU,RVOf (1), r'Y,r′U,r′VAre respectively matrix R'Y,R′U,R′VOf (1).
S7, mixingMatrix R'Y,R′U,R′VDivided into 8 × 8 blocks each containing 64 elements, the 64 elements contained in the blocks are rearranged into a sequence S in a specified direction, as shown in fig. 5.
S8, taking the sequence S as a Markov chain, and calculating a state transition probability matrix M of each sequence S; state transition probability matrix M on Y channelYThe calculation method comprises the following steps:
wherein x and y are integers and have a value range of [0, T]T is a truncation threshold; stThe t-th rearranged sequence, when q is satisfied, delta (q) is equal to 1, otherwise delta (q) is equal to 0, i is the index of 8 × 8 blocks, i is equal to 1,2, …, blknumY,blknumYIs matrix R'YThe number of inner 8 × 8 blocks and for any i,
is a matrix of (T +1) × (T +1) dimensions;
u, V channel state transition probability matrix MU,MVThe calculation method of (2) is the same as that of the Y channel.
S9, calculating the average state transition probability matrix M' in Y, U, V three channelsY,M＇U,M′VAnd connecting the features MM forming a d dimension, wherein d is 3 × (T +1) × (T +1), the dimension d of the features MM is 3 × (T +1) × (T +1), and T is a truncation threshold.
The average state transition probability matrix calculation method comprises the following steps:
MM＝[M′Y,M′U,M′V](7)
wherein MM is a feature of the ciphertext image, blknumY，blknumUAnd blknumVAre respectively matrix R'Y,R′U,R′V8 × 8 block number.
As shown in fig. 6, the image encryption effect of this embodiment is shown in fig. 6(a), fig. 6(b), the image after the VLI code is stream-encrypted in step S2, fig. 6(c), the image after the quantization table is stream-encrypted in step S2, and fig. 6(d), the image after the VLI code and the quantization table are jointly encrypted in step S2.
The foregoing is a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. An encryption and feature extraction method based on JPEG images is characterized in that: the method comprises the following steps:
s1, decoding the original JPEG image to obtain VLI coding and quantization table of the image;
s2, performing stream encryption on VLI codes and quantization tables of the images, and uploading the encrypted images to a cloud server;
s3, after receiving the encrypted image, the cloud server decodes the encrypted JPEG image to obtain VLI code of the image;
s4, restoring the quantized DCT coefficient matrixes Y, U, V by the cloud server, and recording the quantized DCT coefficient matrixes as D respectivelyY,DU,DV；
S5, converting the matrix DY,DU,DVEach element in the code table is converted into a corresponding group number index value according to the VLI code table, and a matrix obtained after conversion is marked as RY,RU,RV；
S6, for the matrix RY,RU,RVThe element in (1) is subjected to truncation processing, and the processed matrix is recorded as R'Y,R′U,R′V；
S7, mixing the matrix R'Y,R′U,R′VDividing the block into a × b, and rearranging a × b elements contained in the block into a sequence S according to a specified direction;
s8, taking the sequence S as a Markov chain, and calculating a state transition probability matrix M of each sequence S;
s9, calculating an average state transition probability matrix M 'within Y, U, V three channels'Y,M′U,M′VAnd connected to form a d-dimensional feature MM.
2. The JPEG image based encryption and feature extraction method as claimed in claim 1, wherein: in step S2, stream encryption is performed on the VLI code, and the encryption algorithm is:
where V is the VLI code of the original image, V' is the encrypted VLI code, eVIs the encryption key that is used to encrypt the data,
for xor operation, and ← for assignment operation.
3. The JPEG image based encryption and feature extraction method as claimed in claim 2, wherein: in step S2, stream encryption is performed on a quantization table, where the quantization table includes a luminance quantization table and a color difference quantization table, and an encryption algorithm is:
in the formula, aYFor luminance quantization tables, QuanUVAs a chroma quantization table, eYAnd eUVRespectively, the encryption keys of the corresponding quantization tables.
4. The JPEG image based encryption and feature extraction method as claimed in claim 1, wherein: in step S6, the truncation algorithm is expressed as follows:
wherein T is a truncation threshold, rY,rU,rVAre respectively a matrix RY,RU,RVOf (1), r'Y,r′U,r′VAre respectively matrix R'Y,R′U,R′YOf (1).
5. The JPEG image based encryption and feature extraction method according to any one of claims 1-4, wherein: in step S8, the state transition probability matrix M on the Y channelYThe calculation method comprises the following steps:
wherein x and y are integers and have a value range of [0, T]T is a truncation threshold; stThe t-th rearranged sequence, when q is satisfied, delta (q) is equal to 1, otherwise delta (q) is equal to 0, i is the index of a × b block, i is equal to 1,2, …, blknumY,blknumYIs matrix R'YThe number of inner a × b blocks and for any i,
is a matrix of (T +1) × (T +1) dimensions;
u, V channel state transition probability matrix MU,MVThe calculation method of (2) is the same as that of the Y channel.
6. The JPEG image based encryption and feature extraction method as claimed in claim 5, wherein: in step S9, the average state transition probability matrix calculation method includes:
MM＝[M′Y,M′U,M′V](7)
wherein MM is a feature of the ciphertext image, blknumY，blknumUAnd blknumVAre respectively matrix R'Y,R′U,R′VThe dimension d of the feature MM is 3 × (T +1) × (T +1), and T is a truncation threshold.
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| CN112417190A (en) * | 2020-11-27 | 2021-02-26 | 暨南大学 | Retrieval method and application of ciphertext JPEG image |
| CN113904764A (en) * | 2021-09-18 | 2022-01-07 | 大连大学 | Image encryption method based on multi-scale compressed sensing and Markov model |
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| US20040136566A1 (en) * | 2002-11-21 | 2004-07-15 | Samsung Electronics Co., Ltd. | Method and apparatus for encrypting and compressing multimedia data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112417190A (en) * | 2020-11-27 | 2021-02-26 | 暨南大学 | Retrieval method and application of ciphertext JPEG image |
| CN113904764A (en) * | 2021-09-18 | 2022-01-07 | 大连大学 | Image encryption method based on multi-scale compressed sensing and Markov model |
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